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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/27816
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| Title: | Data acquisition and cost-effective predictive modeling: targeting
offers for electronic commerce |
| Authors: | Provost, Foster
Melville, Prem
Saar-Tsechansky, Maytal |
| Issue Date: | Aug-2007 |
| Citation: | Proceedings of the Ninth International Conference on Electronic
Commerce, August 2007. |
| Series/Report no.: | CeDER-PP-2007-09 |
| Abstract: | Electronic commerce is revolutionizing the way we think about data
modeling, by making it possible to integrate the processes of (costly)
data acquisition and model induction. The opportunity for improving
modeling through costly data acquisition presents itself for a diverse
set of electronic commerce modeling tasks, from personalization to
customer lifetime value modeling; we illustrate with the running example
of choosing offers to display to web-site visitors, which captures
important aspects in a familiar setting. Considering data acquisition
costs explicitly can allow the building of predictive models at
significantly lower costs, and a modeler may be able to improve
performance via new sources of information that previously were too
expensive to consider. However, existing techniques for integrating
modeling and data acquisition cannot deal with the rich environment that
electronic commerce presents. We discuss several possible data
acquisition settings, the challenges involved in the integration with
modeling, and various research areas that may supply parts of an
ultimate solution. We also present and demonstrate briefly a unified
framework within which one can integrate acquisitions of different
types, with any cost structure and any predictive modeling objective |
| URI: | http://hdl.handle.net/2451/27816 |
| Appears in Collections: | CeDER Published Papers
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